Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory148.6 KiB
Average record size in memory152.1 B

Variable types

Numeric11
Categorical7
DateTime1

Alerts

acs is highly overall correlated with avg_dmg and 3 other fieldsHigh correlation
act is highly overall correlated with game_id and 1 other fieldsHigh correlation
avg_dmg is highly overall correlated with acs and 3 other fieldsHigh correlation
avg_dmg_delta is highly overall correlated with acs and 3 other fieldsHigh correlation
deaths is highly overall correlated with kdr and 1 other fieldsHigh correlation
episode is highly overall correlated with game_id and 1 other fieldsHigh correlation
game_id is highly overall correlated with act and 3 other fieldsHigh correlation
headshot_pct is highly overall correlated with game_idHigh correlation
kdr is highly overall correlated with acs and 4 other fieldsHigh correlation
kills is highly overall correlated with acs and 3 other fieldsHigh correlation
outcome is highly overall correlated with round_losses and 1 other fieldsHigh correlation
rank is highly overall correlated with act and 2 other fieldsHigh correlation
round_losses is highly overall correlated with deaths and 2 other fieldsHigh correlation
round_wins is highly overall correlated with outcome and 1 other fieldsHigh correlation
agent is highly imbalanced (65.8%) Imbalance
game_id is uniformly distributed Uniform
game_id has unique values Unique
assists has 24 (2.4%) zeros Zeros
avg_dmg_delta has 15 (1.5%) zeros Zeros

Reproduction

Analysis started2025-07-19 16:49:47.533765
Analysis finished2025-07-19 16:50:06.391006
Duration18.86 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

game_id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.5
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-19T16:50:06.548342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile950.05
Maximum1000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.57706181
Kurtosis-1.2
Mean500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83416.667
MonotonicityStrictly increasing
2025-07-19T16:50:06.732159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
672 1
 
0.1%
659 1
 
0.1%
660 1
 
0.1%
661 1
 
0.1%
662 1
 
0.1%
663 1
 
0.1%
664 1
 
0.1%
665 1
 
0.1%
666 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%
991 1
0.1%

episode
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
7
310 
9
258 
6
224 
8
208 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
7 310
31.0%
9 258
25.8%
6 224
22.4%
8 208
20.8%

Length

2025-07-19T16:50:06.887486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-19T16:50:07.016200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7 310
31.0%
9 258
25.8%
6 224
22.4%
8 208
20.8%

Most occurring characters

ValueCountFrequency (%)
7 310
31.0%
9 258
25.8%
6 224
22.4%
8 208
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 310
31.0%
9 258
25.8%
6 224
22.4%
8 208
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 310
31.0%
9 258
25.8%
6 224
22.4%
8 208
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 310
31.0%
9 258
25.8%
6 224
22.4%
8 208
20.8%

act
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3
412 
1
346 
2
242 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 412
41.2%
1 346
34.6%
2 242
24.2%

Length

2025-07-19T16:50:07.163671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-19T16:50:07.288992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 412
41.2%
1 346
34.6%
2 242
24.2%

Most occurring characters

ValueCountFrequency (%)
3 412
41.2%
1 346
34.6%
2 242
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 412
41.2%
1 346
34.6%
2 242
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 412
41.2%
1 346
34.6%
2 242
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 412
41.2%
1 346
34.6%
2 242
24.2%

rank
Categorical

High correlation 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Ascendant 1
179 
Diamond 3
153 
Diamond 1
126 
Platinum 3
111 
Gold 3
93 
Other values (9)
338 

Length

Max length11
Median length10
Mean length9.271
Min length6

Characters and Unicode

Total characters9271
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlacement
2nd rowPlacement
3rd rowPlacement
4th rowPlacement
5th rowPlacement

Common Values

ValueCountFrequency (%)
Ascendant 1 179
17.9%
Diamond 3 153
15.3%
Diamond 1 126
12.6%
Platinum 3 111
11.1%
Gold 3 93
9.3%
Diamond 2 78
7.8%
Platinum 1 66
 
6.6%
Ascendant 2 57
 
5.7%
Platinum 2 48
 
4.8%
Placement 24
 
2.4%
Other values (4) 65
 
6.5%

Length

2025-07-19T16:50:07.441518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 388
19.6%
3 364
18.4%
diamond 357
18.1%
ascendant 236
11.9%
platinum 225
11.4%
2 224
11.3%
gold 134
 
6.8%
placement 24
 
1.2%
silver 24
 
1.2%

Most occurring characters

ValueCountFrequency (%)
n 1078
11.6%
976
 
10.5%
a 842
 
9.1%
d 727
 
7.8%
i 606
 
6.5%
m 606
 
6.5%
o 491
 
5.3%
t 485
 
5.2%
l 407
 
4.4%
1 388
 
4.2%
Other values (13) 2665
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9271
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1078
11.6%
976
 
10.5%
a 842
 
9.1%
d 727
 
7.8%
i 606
 
6.5%
m 606
 
6.5%
o 491
 
5.3%
t 485
 
5.2%
l 407
 
4.4%
1 388
 
4.2%
Other values (13) 2665
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9271
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1078
11.6%
976
 
10.5%
a 842
 
9.1%
d 727
 
7.8%
i 606
 
6.5%
m 606
 
6.5%
o 491
 
5.3%
t 485
 
5.2%
l 407
 
4.4%
1 388
 
4.2%
Other values (13) 2665
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9271
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1078
11.6%
976
 
10.5%
a 842
 
9.1%
d 727
 
7.8%
i 606
 
6.5%
m 606
 
6.5%
o 491
 
5.3%
t 485
 
5.2%
l 407
 
4.4%
1 388
 
4.2%
Other values (13) 2665
28.7%

date
Date

Distinct300
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2023-04-11 00:00:00
Maximum2024-11-12 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-19T16:50:07.864775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:08.052911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

agent
Categorical

Imbalance 

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Cypher
719 
Killjoy
230 
Viper
 
17
Omen
 
10
KAY/O
 
9
Other values (6)
 
15

Length

Max length9
Median length6
Mean length6.19
Min length4

Characters and Unicode

Total characters6190
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowCypher
2nd rowCypher
3rd rowKAY/O
4th rowBrimstone
5th rowCypher

Common Values

ValueCountFrequency (%)
Cypher 719
71.9%
Killjoy 230
 
23.0%
Viper 17
 
1.7%
Omen 10
 
1.0%
KAY/O 9
 
0.9%
Brimstone 5
 
0.5%
Breach 3
 
0.3%
Vyse 3
 
0.3%
Astra 2
 
0.2%
Phoenix 1
 
0.1%

Length

2025-07-19T16:50:08.229030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cypher 719
71.9%
killjoy 230
 
23.0%
viper 17
 
1.7%
omen 10
 
1.0%
kay/o 9
 
0.9%
brimstone 5
 
0.5%
breach 3
 
0.3%
vyse 3
 
0.3%
astra 2
 
0.2%
phoenix 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
y 952
15.4%
e 759
12.3%
r 746
12.1%
p 736
11.9%
h 723
11.7%
C 719
11.6%
l 460
7.4%
i 253
 
4.1%
K 239
 
3.9%
o 236
 
3.8%
Other values (17) 367
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
y 952
15.4%
e 759
12.3%
r 746
12.1%
p 736
11.9%
h 723
11.7%
C 719
11.6%
l 460
7.4%
i 253
 
4.1%
K 239
 
3.9%
o 236
 
3.8%
Other values (17) 367
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
y 952
15.4%
e 759
12.3%
r 746
12.1%
p 736
11.9%
h 723
11.7%
C 719
11.6%
l 460
7.4%
i 253
 
4.1%
K 239
 
3.9%
o 236
 
3.8%
Other values (17) 367
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
y 952
15.4%
e 759
12.3%
r 746
12.1%
p 736
11.9%
h 723
11.7%
C 719
11.6%
l 460
7.4%
i 253
 
4.1%
K 239
 
3.9%
o 236
 
3.8%
Other values (17) 367
 
5.9%

map
Categorical

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Ascent
160 
Lotus
143 
Bind
142 
Haven
119 
Split
98 
Other values (6)
338 

Length

Max length8
Median length6
Mean length5.369
Min length4

Characters and Unicode

Total characters5369
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAscent
2nd rowIcebox
3rd rowLotus
4th rowAscent
5th rowHaven

Common Values

ValueCountFrequency (%)
Ascent 160
16.0%
Lotus 143
14.3%
Bind 142
14.2%
Haven 119
11.9%
Split 98
9.8%
Sunset 86
8.6%
Icebox 62
 
6.2%
Pearl 60
 
6.0%
Fracture 53
 
5.3%
Breeze 44
 
4.4%

Length

2025-07-19T16:50:08.393232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ascent 160
16.0%
lotus 143
14.3%
bind 142
14.2%
haven 119
11.9%
split 98
9.8%
sunset 86
8.6%
icebox 62
 
6.2%
pearl 60
 
6.0%
fracture 53
 
5.3%
breeze 44
 
4.4%

Most occurring characters

ValueCountFrequency (%)
e 672
 
12.5%
t 540
 
10.1%
n 507
 
9.4%
s 455
 
8.5%
u 282
 
5.3%
c 275
 
5.1%
i 240
 
4.5%
a 232
 
4.3%
r 210
 
3.9%
o 205
 
3.8%
Other values (16) 1751
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 672
 
12.5%
t 540
 
10.1%
n 507
 
9.4%
s 455
 
8.5%
u 282
 
5.3%
c 275
 
5.1%
i 240
 
4.5%
a 232
 
4.3%
r 210
 
3.9%
o 205
 
3.8%
Other values (16) 1751
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 672
 
12.5%
t 540
 
10.1%
n 507
 
9.4%
s 455
 
8.5%
u 282
 
5.3%
c 275
 
5.1%
i 240
 
4.5%
a 232
 
4.3%
r 210
 
3.9%
o 205
 
3.8%
Other values (16) 1751
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 672
 
12.5%
t 540
 
10.1%
n 507
 
9.4%
s 455
 
8.5%
u 282
 
5.3%
c 275
 
5.1%
i 240
 
4.5%
a 232
 
4.3%
r 210
 
3.9%
o 205
 
3.8%
Other values (16) 1751
32.6%

outcome
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Loss
497 
Win
491 
Draw
 
12

Length

Max length4
Median length4
Mean length3.509
Min length3

Characters and Unicode

Total characters3509
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoss
2nd rowLoss
3rd rowWin
4th rowLoss
5th rowLoss

Common Values

ValueCountFrequency (%)
Loss 497
49.7%
Win 491
49.1%
Draw 12
 
1.2%

Length

2025-07-19T16:50:08.560771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-19T16:50:08.686369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
loss 497
49.7%
win 491
49.1%
draw 12
 
1.2%

Most occurring characters

ValueCountFrequency (%)
s 994
28.3%
L 497
14.2%
o 497
14.2%
W 491
14.0%
i 491
14.0%
n 491
14.0%
D 12
 
0.3%
r 12
 
0.3%
a 12
 
0.3%
w 12
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 994
28.3%
L 497
14.2%
o 497
14.2%
W 491
14.0%
i 491
14.0%
n 491
14.0%
D 12
 
0.3%
r 12
 
0.3%
a 12
 
0.3%
w 12
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 994
28.3%
L 497
14.2%
o 497
14.2%
W 491
14.0%
i 491
14.0%
n 491
14.0%
D 12
 
0.3%
r 12
 
0.3%
a 12
 
0.3%
w 12
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 994
28.3%
L 497
14.2%
o 497
14.2%
W 491
14.0%
i 491
14.0%
n 491
14.0%
D 12
 
0.3%
r 12
 
0.3%
a 12
 
0.3%
w 12
 
0.3%

round_wins
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.589
Minimum0
Maximum18
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-19T16:50:08.811993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median13
Q313
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3599655
Coefficient of variation (CV)0.31730716
Kurtosis-0.1112912
Mean10.589
Median Absolute Deviation (MAD)1
Skewness-0.94816072
Sum10589
Variance11.289368
MonotonicityNot monotonic
2025-07-19T16:50:08.955424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
13 437
43.7%
11 66
 
6.6%
10 65
 
6.5%
9 62
 
6.2%
8 57
 
5.7%
7 48
 
4.8%
6 42
 
4.2%
14 41
 
4.1%
5 40
 
4.0%
4 38
 
3.8%
Other values (9) 104
 
10.4%
ValueCountFrequency (%)
0 1
 
0.1%
1 7
 
0.7%
2 13
 
1.3%
3 14
 
1.4%
4 38
3.8%
5 40
4.0%
6 42
4.2%
7 48
4.8%
8 57
5.7%
9 62
6.2%
ValueCountFrequency (%)
18 1
 
0.1%
17 1
 
0.1%
16 3
 
0.3%
15 26
 
2.6%
14 41
 
4.1%
13 437
43.7%
12 38
 
3.8%
11 66
 
6.6%
10 65
 
6.5%
9 62
 
6.2%

round_losses
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.527
Minimum0
Maximum20
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-19T16:50:09.109234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median13
Q313
95-th percentile14
Maximum20
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5527303
Coefficient of variation (CV)0.33748745
Kurtosis-0.013502633
Mean10.527
Median Absolute Deviation (MAD)1
Skewness-0.99259358
Sum10527
Variance12.621893
MonotonicityNot monotonic
2025-07-19T16:50:09.295086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
13 455
45.5%
10 63
 
6.3%
9 59
 
5.9%
11 53
 
5.3%
8 51
 
5.1%
6 47
 
4.7%
14 44
 
4.4%
7 43
 
4.3%
4 37
 
3.7%
12 33
 
3.3%
Other values (9) 115
 
11.5%
ValueCountFrequency (%)
0 3
 
0.3%
1 12
 
1.2%
2 18
 
1.8%
3 22
 
2.2%
4 37
3.7%
5 31
3.1%
6 47
4.7%
7 43
4.3%
8 51
5.1%
9 59
5.9%
ValueCountFrequency (%)
20 1
 
0.1%
17 2
 
0.2%
16 4
 
0.4%
15 22
 
2.2%
14 44
 
4.4%
13 455
45.5%
12 33
 
3.3%
11 53
 
5.3%
10 63
 
6.3%
9 59
 
5.9%

kills
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.299
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-19T16:50:09.484294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q112
median15
Q319
95-th percentile24
Maximum35
Range34
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.2531128
Coefficient of variation (CV)0.34336315
Kurtosis0.32793095
Mean15.299
Median Absolute Deviation (MAD)3
Skewness0.25428029
Sum15299
Variance27.595194
MonotonicityNot monotonic
2025-07-19T16:50:09.679883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
15 76
 
7.6%
18 76
 
7.6%
14 76
 
7.6%
13 73
 
7.3%
16 72
 
7.2%
17 71
 
7.1%
12 70
 
7.0%
19 57
 
5.7%
11 50
 
5.0%
20 47
 
4.7%
Other values (24) 332
33.2%
ValueCountFrequency (%)
1 2
 
0.2%
2 1
 
0.1%
3 7
 
0.7%
4 3
 
0.3%
5 8
 
0.8%
6 20
2.0%
7 28
2.8%
8 28
2.8%
9 39
3.9%
10 44
4.4%
ValueCountFrequency (%)
35 2
 
0.2%
34 1
 
0.1%
33 2
 
0.2%
31 2
 
0.2%
30 1
 
0.1%
29 1
 
0.1%
28 4
 
0.4%
27 8
0.8%
26 6
 
0.6%
25 17
1.7%

deaths
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.249
Minimum0
Maximum26
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-19T16:50:09.854014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q113
median15
Q317
95-th percentile20
Maximum26
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7243617
Coefficient of variation (CV)0.26137706
Kurtosis0.74452101
Mean14.249
Median Absolute Deviation (MAD)2
Skewness-0.64610133
Sum14249
Variance13.87087
MonotonicityNot monotonic
2025-07-19T16:50:10.015961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
15 146
14.6%
16 136
13.6%
14 120
12.0%
17 107
10.7%
13 88
8.8%
18 66
 
6.6%
11 44
 
4.4%
12 44
 
4.4%
19 39
 
3.9%
10 36
 
3.6%
Other values (17) 174
17.4%
ValueCountFrequency (%)
0 1
 
0.1%
1 1
 
0.1%
2 3
 
0.3%
3 5
 
0.5%
4 2
 
0.2%
5 9
 
0.9%
6 14
1.4%
7 29
2.9%
8 25
2.5%
9 34
3.4%
ValueCountFrequency (%)
26 1
 
0.1%
25 2
 
0.2%
24 1
 
0.1%
23 1
 
0.1%
22 7
 
0.7%
21 14
 
1.4%
20 25
 
2.5%
19 39
 
3.9%
18 66
6.6%
17 107
10.7%

assists
Real number (ℝ)

Zeros 

Distinct18
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.656
Minimum0
Maximum20
Zeros24
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-19T16:50:10.161775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.656072
Coefficient of variation (CV)0.57046221
Kurtosis2.6753111
Mean4.656
Median Absolute Deviation (MAD)2
Skewness1.0786992
Sum4656
Variance7.0547187
MonotonicityNot monotonic
2025-07-19T16:50:10.321333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4 171
17.1%
3 155
15.5%
5 148
14.8%
6 115
11.5%
2 113
11.3%
7 89
8.9%
1 65
 
6.5%
8 51
 
5.1%
0 24
 
2.4%
9 20
 
2.0%
Other values (8) 49
 
4.9%
ValueCountFrequency (%)
0 24
 
2.4%
1 65
 
6.5%
2 113
11.3%
3 155
15.5%
4 171
17.1%
5 148
14.8%
6 115
11.5%
7 89
8.9%
8 51
 
5.1%
9 20
 
2.0%
ValueCountFrequency (%)
20 1
 
0.1%
18 2
 
0.2%
15 1
 
0.1%
14 4
 
0.4%
13 4
 
0.4%
12 7
 
0.7%
11 12
 
1.2%
10 18
 
1.8%
9 20
 
2.0%
8 51
5.1%

kdr
Real number (ℝ)

High correlation 

Distinct41
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1784
Minimum0.2
Maximum9.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-19T16:50:10.496744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.5
Q10.8
median1.1
Q31.4
95-th percentile2.3
Maximum9.7
Range9.5
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.65678368
Coefficient of variation (CV)0.55735207
Kurtosis36.490696
Mean1.1784
Median Absolute Deviation (MAD)0.3
Skewness4.0213995
Sum1178.4
Variance0.4313648
MonotonicityNot monotonic
2025-07-19T16:50:10.687432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1.1 117
11.7%
0.8 110
11.0%
0.9 99
 
9.9%
1.2 69
 
6.9%
0.7 64
 
6.4%
1.3 61
 
6.1%
1 61
 
6.1%
0.6 58
 
5.8%
1.4 52
 
5.2%
1.5 43
 
4.3%
Other values (31) 266
26.6%
ValueCountFrequency (%)
0.2 4
 
0.4%
0.3 9
 
0.9%
0.4 27
 
2.7%
0.5 40
 
4.0%
0.6 58
5.8%
0.7 64
6.4%
0.8 110
11.0%
0.9 99
9.9%
1 61
6.1%
1.1 117
11.7%
ValueCountFrequency (%)
9.7 1
0.1%
6.5 1
0.1%
6 1
0.1%
4.5 1
0.1%
4.3 1
0.1%
4 1
0.1%
3.7 1
0.1%
3.6 1
0.1%
3.5 1
0.1%
3.3 1
0.1%

avg_dmg_delta
Real number (ℝ)

High correlation  Zeros 

Distinct194
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.308
Minimum-124
Maximum293
Zeros15
Zeros (%)1.5%
Negative404
Negative (%)40.4%
Memory size7.9 KiB
2025-07-19T16:50:10.869599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-124
5-th percentile-59
Q1-20
median9
Q339
95-th percentile82.05
Maximum293
Range417
Interquartile range (IQR)59

Descriptive statistics

Standard deviation43.383791
Coefficient of variation (CV)4.2087496
Kurtosis1.7676368
Mean10.308
Median Absolute Deviation (MAD)30
Skewness0.47937047
Sum10308
Variance1882.1533
MonotonicityNot monotonic
2025-07-19T16:50:11.037204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 16
 
1.6%
17 16
 
1.6%
30 16
 
1.6%
0 15
 
1.5%
-17 14
 
1.4%
-2 14
 
1.4%
2 14
 
1.4%
10 13
 
1.3%
22 12
 
1.2%
-13 12
 
1.2%
Other values (184) 858
85.8%
ValueCountFrequency (%)
-124 1
0.1%
-99 1
0.1%
-98 1
0.1%
-89 1
0.1%
-88 1
0.1%
-87 1
0.1%
-86 1
0.1%
-85 1
0.1%
-81 1
0.1%
-77 2
0.2%
ValueCountFrequency (%)
293 1
0.1%
190 1
0.1%
153 1
0.1%
152 1
0.1%
140 2
0.2%
136 1
0.1%
126 1
0.1%
123 1
0.1%
119 1
0.1%
117 1
0.1%

headshot_pct
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.763
Minimum2
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-19T16:50:11.220054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q115
median22
Q329
95-th percentile40.05
Maximum63
Range61
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.9190331
Coefficient of variation (CV)0.43575245
Kurtosis0.36613888
Mean22.763
Median Absolute Deviation (MAD)7
Skewness0.56162459
Sum22763
Variance98.387218
MonotonicityNot monotonic
2025-07-19T16:50:11.412944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 52
 
5.2%
18 42
 
4.2%
29 42
 
4.2%
22 42
 
4.2%
25 42
 
4.2%
15 38
 
3.8%
26 38
 
3.8%
19 37
 
3.7%
21 35
 
3.5%
27 34
 
3.4%
Other values (44) 598
59.8%
ValueCountFrequency (%)
2 1
 
0.1%
3 4
 
0.4%
4 4
 
0.4%
5 9
 
0.9%
6 8
 
0.8%
7 7
 
0.7%
8 26
2.6%
9 16
1.6%
10 24
2.4%
11 24
2.4%
ValueCountFrequency (%)
63 1
 
0.1%
61 1
 
0.1%
60 1
 
0.1%
57 2
0.2%
54 1
 
0.1%
52 1
 
0.1%
50 4
0.4%
48 1
 
0.1%
47 3
0.3%
46 2
0.2%

avg_dmg
Real number (ℝ)

High correlation 

Distinct167
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.446
Minimum42
Maximum373
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-19T16:50:11.596204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile79
Q1110
median133
Q3157
95-th percentile192
Maximum373
Range331
Interquartile range (IQR)47

Descriptive statistics

Standard deviation35.199916
Coefficient of variation (CV)0.26181453
Kurtosis2.0209744
Mean134.446
Median Absolute Deviation (MAD)24
Skewness0.5087467
Sum134446
Variance1239.0341
MonotonicityNot monotonic
2025-07-19T16:50:11.782709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 16
 
1.6%
140 16
 
1.6%
152 16
 
1.6%
145 16
 
1.6%
133 15
 
1.5%
100 15
 
1.5%
135 15
 
1.5%
144 15
 
1.5%
128 13
 
1.3%
113 13
 
1.3%
Other values (157) 850
85.0%
ValueCountFrequency (%)
42 2
0.2%
52 1
0.1%
53 1
0.1%
55 1
0.1%
56 1
0.1%
57 1
0.1%
58 1
0.1%
60 2
0.2%
61 1
0.1%
62 1
0.1%
ValueCountFrequency (%)
373 1
0.1%
284 1
0.1%
266 1
0.1%
251 1
0.1%
240 1
0.1%
229 1
0.1%
228 1
0.1%
221 2
0.2%
216 2
0.2%
211 1
0.1%

acs
Real number (ℝ)

High correlation 

Distinct245
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.382
Minimum59
Maximum572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-19T16:50:11.957000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile118.95
Q1166
median203
Q3240
95-th percentile299.05
Maximum572
Range513
Interquartile range (IQR)74

Descriptive statistics

Standard deviation56.539958
Coefficient of variation (CV)0.27529169
Kurtosis2.0060938
Mean205.382
Median Absolute Deviation (MAD)37
Skewness0.55158094
Sum205382
Variance3196.7668
MonotonicityNot monotonic
2025-07-19T16:50:12.152756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192 11
 
1.1%
183 11
 
1.1%
181 11
 
1.1%
147 10
 
1.0%
226 10
 
1.0%
240 10
 
1.0%
177 10
 
1.0%
194 10
 
1.0%
214 10
 
1.0%
184 10
 
1.0%
Other values (235) 897
89.7%
ValueCountFrequency (%)
59 1
0.1%
67 1
0.1%
69 1
0.1%
70 1
0.1%
72 1
0.1%
74 1
0.1%
76 1
0.1%
80 2
0.2%
81 1
0.1%
82 1
0.1%
ValueCountFrequency (%)
572 1
0.1%
493 1
0.1%
407 1
0.1%
381 1
0.1%
362 1
0.1%
352 1
0.1%
349 1
0.1%
344 1
0.1%
343 1
0.1%
341 1
0.1%

num_frag
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
225 
4
209 
3
207 
5
181 
1
178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row5
4th row2
5th row4

Common Values

ValueCountFrequency (%)
2 225
22.5%
4 209
20.9%
3 207
20.7%
5 181
18.1%
1 178
17.8%

Length

2025-07-19T16:50:12.342920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-19T16:50:12.481284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 225
22.5%
4 209
20.9%
3 207
20.7%
5 181
18.1%
1 178
17.8%

Most occurring characters

ValueCountFrequency (%)
2 225
22.5%
4 209
20.9%
3 207
20.7%
5 181
18.1%
1 178
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 225
22.5%
4 209
20.9%
3 207
20.7%
5 181
18.1%
1 178
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 225
22.5%
4 209
20.9%
3 207
20.7%
5 181
18.1%
1 178
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 225
22.5%
4 209
20.9%
3 207
20.7%
5 181
18.1%
1 178
17.8%

Interactions

2025-07-19T16:50:04.364404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:48.587593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:50.141747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:51.755002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:53.267423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:54.772545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:56.406134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:57.978407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:59.462433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:01.326727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:02.883384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:04.485821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:48.747838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:50.271727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:51.886798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:53.397819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:54.891144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:56.531075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:58.097924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:59.583334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:01.463808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:03.011746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:04.621115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:48.883636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:50.407912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:52.025319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:53.542177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:55.025190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:56.674148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:58.228316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:59.720271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:01.618198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:03.157398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:04.759448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:49.023443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:50.544230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:52.179884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:53.686245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:55.201468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:56.818002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:58.370303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:59.861705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:01.767757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:03.303865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:04.892130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:49.179737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:50.682252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:52.331919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:53.815124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:55.326639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:56.956068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:58.499737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:59.989818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:01.899976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:03.434785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:05.026248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:49.347915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:50.807366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:52.465807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:53.944369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:55.445446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:57.120665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:58.629487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:00.128909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:02.029335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:03.570230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:05.207697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:49.489576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:50.948612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:52.601379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:54.087853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:55.580639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:57.279553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:58.766747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:00.284505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:02.206825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:03.706129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:05.333510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:49.636158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:51.076959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:52.745424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:54.242611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:55.889707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:57.417256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:58.909492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:00.437800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:02.349809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:03.844295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:05.447215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:49.769963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:51.210092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:52.868026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:54.381106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:56.017085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:57.580946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:59.033391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:00.595573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:02.480970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:03.973509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:05.571238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:49.894637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:51.350315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:53.003052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:54.517850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:56.150025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:57.717295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:59.197188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:00.763571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:02.617631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:04.112774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:05.700005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:50.024902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:51.628545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:53.135976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:54.652338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:56.287522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:57.852376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:49:59.340162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:00.933180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:02.760040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-19T16:50:04.245597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-07-19T16:50:12.614839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
acsactagentassistsavg_dmgavg_dmg_deltadeathsepisodegame_idheadshot_pctkdrkillsmapnum_fragoutcomerankround_lossesround_wins
acs1.0000.0510.0000.1110.9610.833-0.1470.000-0.0340.1420.7810.8270.0710.4910.1140.059-0.1560.204
act0.0511.0000.2550.0490.0760.0000.0530.3980.6160.1580.0550.0660.1570.0590.0000.5200.0000.053
agent0.0000.2551.0000.2330.0780.0310.0000.3100.2220.1260.0000.0300.2120.0000.0000.2500.0000.000
assists0.1110.0490.2331.0000.1260.0920.1880.046-0.126-0.1840.0290.1610.0490.0370.1400.067-0.0350.339
avg_dmg0.9610.0760.0780.1261.0000.848-0.1160.0000.0010.1750.7290.7920.0770.4310.1090.070-0.1310.197
avg_dmg_delta0.8330.0000.0310.0920.8481.000-0.4290.0570.0410.2110.9010.7090.0000.3010.2920.076-0.4130.407
deaths-0.1470.0530.0000.188-0.116-0.4291.0000.000-0.047-0.088-0.5150.1000.0410.0160.4470.0510.742-0.132
episode0.0000.3980.3100.0460.0000.0570.0001.0000.9030.3650.0000.0550.3380.0000.0000.5950.0000.024
game_id-0.0340.6160.222-0.1260.0010.041-0.0470.9031.0000.5840.018-0.0110.2130.0540.0000.5040.022-0.031
headshot_pct0.1420.1580.126-0.1840.1750.211-0.0880.3650.5841.0000.1900.1550.1340.0820.0000.180-0.009-0.007
kdr0.7810.0550.0000.0290.7290.901-0.5150.0000.0180.1901.0000.7290.0000.2350.3180.053-0.4810.473
kills0.8270.0660.0300.1610.7920.7090.1000.055-0.0110.1550.7291.0000.0180.4200.2220.0000.0310.416
map0.0710.1570.2120.0490.0770.0000.0410.3380.2130.1340.0000.0181.0000.0610.0770.1300.0230.000
num_frag0.4910.0590.0000.0370.4310.3010.0160.0000.0540.0820.2350.4200.0611.0000.0300.0390.0410.000
outcome0.1140.0000.0000.1400.1090.2920.4470.0000.0000.0000.3180.2220.0770.0301.0000.0000.7390.715
rank0.0590.5200.2500.0670.0700.0760.0510.5950.5040.1800.0530.0000.1300.0390.0001.0000.0000.000
round_losses-0.1560.0000.000-0.035-0.131-0.4130.7420.0000.022-0.009-0.4810.0310.0230.0410.7390.0001.000-0.521
round_wins0.2040.0530.0000.3390.1970.407-0.1320.024-0.031-0.0070.4730.4160.0000.0000.7150.000-0.5211.000

Missing values

2025-07-19T16:50:05.887800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-19T16:50:06.214270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

game_idepisodeactrankdateagentmapoutcomeround_winsround_losseskillsdeathsassistskdravg_dmg_deltaheadshot_pctavg_dmgacsnum_frag
0162Placement4/11/2023CypherAscentLoss51381540.5-613761253
1262Placement4/12/2023CypherIceboxLoss41331520.2-1242242595
2362Placement4/15/2023KAY/OLotusWin13471270.6-462871325
3462Placement4/15/2023BrimstoneAscentLoss9131812101.523141372302
4562Placement4/15/2023CypherHavenLoss11361430.4-757901464
5662Silver 24/15/2023KAY/OHavenWin13101615181.119101552343
6762Silver 24/15/2023CypherPearlLoss31371550.5-43111121454
7862Silver 24/15/2023KAY/OIceboxLoss41381540.5-703731195
8962Silver 24/16/2023CypherPearlLoss1013141740.8-329991604
91062Silver 24/16/2023PhoenixFractureWin13416762.311182112842
game_idepisodeactrankdateagentmapoutcomeround_winsround_losseskillsdeathsassistskdravg_dmg_deltaheadshot_pctavg_dmgacsnum_frag
99099193Diamond 211/7/2024CypherAbyssLoss713211521.45331602791
99199293Diamond 211/7/2024OmenBindLoss713121670.8-26571081743
99299393Diamond 211/9/2024CypherAscentLoss41371400.5-5929731094
99399493Diamond 211/10/2024OmenAscentLoss8131414111.0-19301271913
99499593Diamond 211/10/2024OmenSplitLoss513111540.7-38151131873
99599693Diamond 211/11/2024AstraPearlWin139171381.330191332124
99699793Diamond 211/11/2024OmenAscentLoss11132714111.941281762991
99799893Diamond 211/11/2024AstraPearlLoss613121650.80471431942
99899993Diamond 211/12/2024OmenSplitWin1310181381.426311342145
999100093Diamond 211/12/2024ViperBindLoss71381740.5-7118871304